Week 1

• Efficiently clean and standardize individual-level case data (i.e., line lists): standardise different date and number formats, understand patterns of missing data, check sequence of dated events, and perform dictionary-based substitutions from clean hand entered categories like sex or location.
• Protect your real-time analysis against frequent changes and updates in input data like new entries, new variables, and renamed variables, by using validation rules.
• Produce epidemic curves to explore patterns of disease spread by different groups and time intervals using individual-level case data (i.e., line lists).
• Apply best practices for reproducible analysis and reporting during an outbreak.

Week 2

• Access and analyse common epidemiological parameters (e.g., incubation period, serial interval, and epidemiological delays) from open-source literature search databases stored in newly developed R packages.
• Estimate key transmission metrics, such as the reproduction number, from real-time or retrospective surveillance data.
• Produce short-term forecasts that account for reporting delays, noise and uncertainty in outbreak reporting.
• Estimate the case fatality risk (CFR) from individual-level and aggregated incidence case and death data, adjusting for delays between onset of symptoms and disease outcome.

Week 3

• Estimate and compare the dispersion parameter, a critical measure of superspreading events, from contact tracing data.
• Summarise the proportion of transmission that is linked to 'superspreading events', and implications for forwards and backwards contact tracing.
• Generate short-term simulated projections that account for randomness in early outbreak transmission.

Week 4

• Access and analyse reported epidemiologically relevant social contact mixing data from different countries.
• Assess outbreak risk (i.e., the potential for sustained transmission) under different immunisation scenarios, accounting for age patterns of social contact and vaccine coverage.
• Create simulated epidemic scenarios that account for population structure to compare the impact of non-pharmaceutical and pharmaceutical interventions.
• Produce scenario visualisations to communicate key findings to wider audiences

This curriculum features a range of methods and R packages that have been used previously in real-life outbreak preparedness and response, including open-source packages from the Epiverse-TRACE, Epiforecasts, and Reconverse toolkits.